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Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More [Paperback]

Matthew A. Russell
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Table of Contents

Preface; README.1st; Managing Your Expectations; Python-Centric Technology; Improvements Specific to the Second Edition; Conventions Used in This Book; Using Code Examples; Safari® Books Online; How to Contact Us; Acknowledgments for the Second Edition; Acknowledgments from the First Edition; A Guided Tour of the Social Web; Prelude; Chapter 1: Mining Twitter: Exploring Trending Topics, Discovering What People Are Talking About, and More; 1.1 Overview; 1.2 Why Is Twitter All the Rage?; 1.3 Exploring Twitter's API; 1.4 Analyzing the 140 Characters; 1.5 Closing Remarks; 1.6 Recommended Exercises; 1.7 Online Resources; Chapter 2: Mining Facebook: Analyzing Fan Pages, Examining Friendships, and More; 2.1 Overview; 2.2 Exploring Facebook's Social Graph API; 2.3 Analyzing Social Graph Connections; 2.4 Closing Remarks; 2.5 Recommended Exercises; 2.6 Online Resources; Chapter 3: Mining LinkedIn: Faceting Job Titles, Clustering Colleagues, and More; 3.1 Overview; 3.2 Exploring the LinkedIn API; 3.3 Crash Course on Clustering Data; 3.4 Closing Remarks; 3.5 Recommended Exercises; 3.6 Online Resources; Chapter 4: Mining Google+: Computing Document Similarity, Extracting Collocations, and More; 4.1 Overview; 4.2 Exploring the Google+ API; 4.3 A Whiz-Bang Introduction to TF-IDF; 4.4 Querying Human Language Data with TF-IDF; 4.5 Closing Remarks; 4.6 Recommended Exercises; 4.7 Online Resources; Chapter 5: Mining Web Pages: Using Natural Language Processing to Understand Human Language, Summarize Blog Posts, and More; 5.1 Overview; 5.2 Scraping, Parsing, and Crawling the Web; 5.3 Discovering Semantics by Decoding Syntax; 5.4 Entity-Centric Analysis: A Paradigm Shift; 5.5 Quality of Analytics for Processing Human Language Data; 5.6 Closing Remarks; 5.7 Recommended Exercises; 5.8 Online Resources; Chapter 6: Mining Mailboxes: Analyzing Who's Talking to Whom About What, How Often, and More; 6.1 Overview; 6.2 Obtaining and Processing a Mail Corpus; 6.3 Analyzing the Enron Corpus; 6.4 Discovering and Visualizing Time-Series Trends; 6.5 Analyzing Your Own Mail Data; 6.6 Closing Remarks; 6.7 Recommended Exercises; 6.8 Online Resources; Chapter 7: Mining GitHub: Inspecting Software Collaboration Habits, Building Interest Graphs, and More; 7.1 Overview; 7.2 Exploring GitHub's API; 7.3 Modeling Data with Property Graphs; 7.4 Analyzing GitHub Interest Graphs; 7.5 Closing Remarks; 7.6 Recommended Exercises; 7.7 Online Resources; Chapter 8: Mining the Semantically Marked-Up Web: Extracting Microformats, Inferencing over RDF, and More; 8.1 Overview; 8.2 Microformats: Easy-to-Implement Metadata; 8.3 From Semantic Markup to Semantic Web: A Brief Interlude; 8.4 The Semantic Web: An Evolutionary Revolution; 8.5 Closing Remarks; 8.6 Recommended Exercises; 8.7 Online Resources; Twitter Cookbook; Chapter 9: Twitter Cookbook; 9.1 Accessing Twitter's API for Development Purposes; 9.2 Doing the OAuth Dance to Access Twitter’s API for Production Purposes; 9.3 Discovering the Trending Topics; 9.4 Searching for Tweets; 9.5 Constructing Convenient Function Calls; 9.6 Saving and Restoring JSON Data with Text Files; 9.7 Saving and Accessing JSON Data with MongoDB; 9.8 Sampling the Twitter Firehose with the Streaming API; 9.9 Collecting Time-Series Data; 9.10 Extracting Tweet Entities; 9.11 Finding the Most Popular Tweets in a Collection of Tweets; 9.12 Finding the Most Popular Tweet Entities in a Collection of Tweets; 9.13 Tabulating Frequency Analysis; 9.14 Finding Users Who Have Retweeted a Status; 9.15 Extracting a Retweet’s Attribution; 9.16 Making Robust Twitter Requests; 9.17 Resolving User Profile Information; 9.18 Extracting Tweet Entities from Arbitrary Text; 9.19 Getting All Friends or Followers for a User; 9.20 Analyzing a User’s Friends and Followers; 9.21 Harvesting a User’s Tweets; 9.22 Crawling a Friendship Graph; 9.23 Analyzing Tweet Content; 9.24 Summarizing Link Targets; 9.25 Analyzing a User’s Favorite Tweets; 9.26 Closing Remarks; 9.27 Recommended Exercises; 9.28 Online Resources; Appendixes; Information About This Book's Virtual Machine Experience; OAuth Primer; Overview; Python and IPython Notebook Tips & Tricks; Colophon;|

  • Preface
  • A Guided Tour of the Social Web
    • Prelude
    • Chapter 1: Mining Twitter: Exploring Trending Topics, Discovering What People Are Talking About, and More
    • Chapter 2: Mining Facebook: Analyzing Fan Pages, Examining Friendships, and More
    • Chapter 3: Mining LinkedIn: Faceting Job Titles, Clustering Colleagues, and More
    • Chapter 4: Mining Google+: Computing Document Similarity, Extracting Collocations, and More
    • Chapter 5: Mining Web Pages: Using Natural Language Processing to Understand Human Language, Summarize Blog Posts, and More
    • Chapter 6: Mining Mailboxes: Analyzing Who's Talking to Whom About What, How Often, and More
    • Chapter 7: Mining GitHub: Inspecting Software Collaboration Habits, Building Interest Graphs, and More
    • Chapter 8: Mining the Semantically Marked-Up Web: Extracting Microformats, Inferencing over RDF, and More
  • Twitter Cookbook
    • Chapter 9: Twitter Cookbook
  • Appendixes
    • Information About This Book's Virtual Machine Experience
    • OAuth Primer
    • Python and IPython Notebook Tips & Tricks
  • Colophon

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